119 citations · 191 across the 12 of their papers we have counts for
12 papers
AdaNeRF: Adaptive Sampling for Real-time Rendering of Neural Radiance Fields
Andreas Kurz, Thomas Neff, Zhaoyang Lv +2
Novel view synthesis has recently been revolutionized by learning neural radiance fields directly from sparse observations. However, rendering images with this new paradigm is slow…
LiveView: Dynamic Target-Centered MPI for View Synthesis
Sushobhan Ghosh, Zhaoyang Lv, Nathan Matsuda +3
Existing Multi-Plane Image (MPI) based view-synthesis methods generate an MPI aligned with the input view using a fixed number of planes in one forward pass. These methods produce…
STaR: Self-supervised Tracking and Reconstruction of Rigid Objects in Motion with Neural Rendering
Wentao Yuan, Zhaoyang Lv, Tanner Schmidt +1
We present STaR, a novel method that performs Self-supervised Tracking and Reconstruction of dynamic scenes with rigid motion from multi-view RGB videos without any manual annotati…
SENSE: a Shared Encoder Network for Scene-flow Estimation
Huaizu Jiang, Deqing Sun, Varun Jampani +3
We introduce a compact network for holistic scene flow estimation, called SENSE, which shares common encoder features among four closely-related tasks: optical flow estimation, dis…
miniSAM: A Flexible Factor Graph Non-linear Least Squares Optimization Framework
Jing Dong, Zhaoyang Lv
Many problems in computer vision and robotics can be phrased as non-linear least squares optimization problems represented by factor graphs, for example, simultaneous localization…
Multi-class Classification without Multi-class Labels
Yen-Chang Hsu, Zhaoyang Lv, Joel Schlosser +2
This work presents a new strategy for multi-class classification that requires no class-specific labels, but instead leverages pairwise similarity between examples, which is a weak…
Taking a Deeper Look at the Inverse Compositional Algorithm
Zhaoyang Lv, Frank Dellaert, James M. Rehg +1
In this paper, we provide a modern synthesis of the classic inverse compositional algorithm for dense image alignment. We first discuss the assumptions made by this well-establishe…
A probabilistic constrained clustering for transfer learning and image category discovery
Yen-Chang Hsu, Zhaoyang Lv, Joel Schlosser +2
Neural network-based clustering has recently gained popularity, and in particular a constrained clustering formulation has been proposed to perform transfer learning and image cate…
Learning Rigidity in Dynamic Scenes with a Moving Camera for 3D Motion Field Estimation
Zhaoyang Lv, Kihwan Kim, Alejandro Troccoli +3
Estimation of 3D motion in a dynamic scene from a temporal pair of images is a core task in many scene understanding problems. In real world applications, a dynamic scene is common…
Learning to cluster in order to transfer across domains and tasks
Yen-Chang Hsu, Zhaoyang Lv, Zsolt Kira
This paper introduces a novel method to perform transfer learning across domains and tasks, formulating it as a problem of learning to cluster. The key insight is that, in addition…
Deep Image Category Discovery using a Transferred Similarity Function
Yen-Chang Hsu, Zhaoyang Lv, Zsolt Kira
Automatically discovering image categories in unlabeled natural images is one of the important goals of unsupervised learning. However, the task is challenging and even human being…
A Continuous Optimization Approach for Efficient and Accurate Scene Flow
Zhaoyang Lv, Chris Beall, Pablo F. Alcantarilla +3
We propose a continuous optimization method for solving dense 3D scene flow problems from stereo imagery. As in recent work, we represent the dynamic 3D scene as a collection of ri…